1. Long-lead flood forecasting for India: challenges, opportunities, outline Tom Hopson

2. “Science exists to serve human welfare. It’s wonderful to have the opportunity given us by society to do basic research, but in return, we have a very important moral responsibility to apply that research to benefiting humanity.” Dr. Walter Orr Roberts (NCAR founder)

3. NCAR Scientific facilities 2. Supercomputers, data and networks 3. International Collaborative Research Environment National Science Foundation Research & Development Center - 900 Staff, 500 Scientists/Engineers - Basic Research & Societal Applications - Atmospheric and related sciences 1. Advanced Observational Facilities

4. universities -- NCAR board composed exclusively of US universities

5. Global Climate Models

7. Overview: I.Challenges i.Natural ii.Observational limitations II.Technological Opportunities III.Overview of this week’s course Primary challenge in forecasting river flow: I.estimating and forecasting precipitation And II. measurement of upstream river conditions

8. 8 Natural Challenge: Topography Complete river basin monitoring difficult in Northern sections of major watersheds: –Rain gauge installation and monitoring –River gauging location –Snow gauging location

9. 9 Monitoring basin’s available soil moisture not done in “real-time”! => Data collection problem!

10. 10 Natural Challenge: Topography Weather precipitation radar for future monitoring and instrumentation needs (predominantly used in the US): => Topography causes radar signal blockage, limiting coverage Doppler radar (e.g. Calcutta) providing adequate coverage in places?

11. 11 Natural Challenge: Topography Use of numerical weather prediction forecast output to “fill in” the instrumentation gaps or for advanced lead- time flood forecasting … but has own set of challenges in mountainous environments …

12. => Use caution with numerical weather prediction outputs

13. Trans-boundary challenges: Parts of watersheds in other countries Q: Data sharing of both rain and river gauge? How reliable and how quickly? Opportunities for further engagement? Current method: lagged correlation of stage with border Q (8hr forecast?)

14. 14 Parts of basins snow dominated: -- complicated variable to model and measure

15. “Historical challenges”: Low density of -rain gauges -river gauges Lack of telemetric reporting => Basis of (US) traditional flood forecasting approaches Q: what is the density in your basin? How many develop rating curves?

16. … more “Historical challenges”: Maintaining updated rating curves --- important for hydrologic (watershed) model calibration and state proper variable for river routing (e.g. not stage) (sediment load issues) sufficient radars (basis of US monitoring)

17. Opportunities: Snow covered basins -- latent predictability

18. 18 -- latent predictability … for snow dominated basins

19. Opportunities: Snow covered basins -- latent predictability Remotely-sensed (satellite) data -Discharge -Rain -Snow

20. MODIS in the West -- snow covered area Yampa Basin, Colorado Missing Cloud Snow Snow-Free Snow covered area …

21. The Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) is a twelve-channel, six- frequency, passive-microwave radiometer system. It measures horizontally and vertically polarized brightness temperatures at 6.9 GHz, 10.7 GHz, 18.7 GHz, 23.8 GHz, 36.5 GHz, and 89.0 GHz. Spatial resolution of the individual measurements varies from 5.4 km at 89 GHz to 56 km at 6.9 GHz. AMSR-E was developed by the Japan Aerospace Exploration Agency (JAXA) and launched by the U.S. aboard Aqua in mid-2002. Objective Monitoring of River Status: The Microwave Solution

22. Example: Wabash River near Mount Carmel, Indiana, USA Black square shows Measurement pixel (blue line in next plot) White square is calibration pixel (green line in next plot) Dark blue colors: mapped flooding New: latency of 6-8hr! Dartmouth Flood Observatory Approach Discharge …

23. Satellite Precipitation Products Monsoon season (Aug 1, 2004) Indian subcontinent TRMM Rainfall … data roughly 6hr-delayed. IR-based data 15min delays

24. Gravity Recovery And Climate Experiment (GRACE) Slide from Sean Swenson, NCAR

25. GRACE catchment-integrated soil moisture estimates useful for: 1) Hydrologic model calibration and validation 2) Seasonal forecasting 3) Data assimilation for medium-range (1-2 week) forecasts Slide from Sean Swenson, NCAR

27. Opportunities: Snow covered basins -- latent predictability Remotely-sensed (satellite) data Large-scale features of the monsoon -- predictability ENSO, MJO

28. slide from Peter Webster

29. (Peter Webster)

30. Opportunities: Snow covered basins -- latent predictability Remotely-sensed (satellite) data Large-scale features of the monsoon -- predictability ENSO, MJO Modeling developments

31. Numerical Weather Prediction continues to improve … - ECMWF GCM or NCAR’s WRF

32. -- Weather forecast skill (RMS error) increases with spatial (and temporal) scale => Utility of weather forecasts in flood forecasting increases for larger catchments -- Logarithmic increase Rule of Thumb:

33. Opportunities: Snow covered basins -- latent predictability Remotely-sensed (satellite) data Large-scale features of the monsoon -- predictability ENSO, MJO Modeling developments Blending models with local and remotely-sensed data sets

34. Data Assimilation: The Basics Improve knowledge of Initial conditions Assimilate observations at time t Model “relocated” to new position

36. Bangladesh Flood Forecasting

37. Opportunities: Snow covered basins -- latent predictability Remotely-sensed (satellite) data Large-scale features of the monsoon -- predictability ENSO, MJO Modeling developments Blending models with local data sets Institutional commitment to capacity build up Scientific and engineering talent of India

39. Day1 Session 1 -- overview of course -- Introductions of participants and questionnaire Session 2 -- CFAB example Session 3 -- introduction to linux: shell commands, cron Session 4 -- introduction to R Course Outline Day2 Session 1 -- QPE products -- rain and snow gauges -- radar -- satellite precip -- QPF products -- NWP -- GCM and mesoscale atmospheric models -- ensemble forecasting Session 2 -- preprocessing -- bias removal and types/sources of stochastic behavior/uncertainty -- quantile-to-quantile matching -- deterministic processing and particularities of precip/wind speed -- ensemble products and making statistically-equiv Session 3 -- Introduction to IDL Session 4 -- wget and download satellite precip and cron -- quantile-to-quantile matching

40. Day3 Session 1 -- hydrologic models and their plusses/minuses -- lumped model -- time-series analysis -- overcalibration and cross-validation and information criteria Session 2 -- distributed model -- numerical methods -- calibration and over-calibration Session 3 -- time-series analysis -- AR, ARMA, ARIMA, and other types of models -- overfitting, information criteria, and cross-validation Session 4 -- numerical methods and 2-layer models -- multi-modeling Course Outline (cont) Day4 Session 1 -- multi-model -- post-processing -- BMA/KNN/QR/LR Session 2 -- verification -- user needs Session 3 -- post-processing algorithms via R Session 4 -- running full CFAB codes -- verification

41. Goals: 1)Introduction (brief) on advanced techniques being implemented for flood forecasting – many are still evolving in their effectiveness, so be discriminating! 2) Awareness of (new) global data sets available for use 3) Awareness of available and relevant software tools Stress: stay simple and only add complexity *if* needed. Stay focused on your goals. Do you have what you need already, both in terms of data and tools (have you adequately tested them)? If not, prioritize and build from the simple. e.g. calibrating rainfall at a point versus for the whole watershed.

42. Next up: Linux – why learn new OS for flood forecasting? - powerful, with easily automated processes - most-used scientific and engineering tool development and computational environment - efficient - free (sort of)! R – why? - powerful cutting-edge statistical tools (e.g. post-processing techniques, parametric and non-parametric tools and regression analysis, verification, extreme-value analysis - efficient (not so) - free (sort of)!